MRI Brain Image Segmentation Using Level Set Methods

In this documentation, the author demonstrates an approach for MRI brain image segmentation utilizing level set methods, accompanied by comprehensive instructions featuring a graphical interface. This implementation employs active contour models where the level set function evolves according to partial differential equations to precisely capture brain tissue boundaries. The method enables more accurate segmentation of brain images, allowing medical professionals and researchers to better analyze patient conditions and brain structures. The solution includes practical application examples with code snippets showcasing key functions such as curvature calculation, speed function initialization, and re-initialization procedures. The author provides implementation details for handling common challenges including intensity inhomogeneity correction and contour initialization techniques. Additionally, the documentation addresses frequent issues encountered during implementation and offers troubleshooting solutions to help users avoid common pitfalls. Notable algorithmic components discussed include: - Implementation of distance regularization for stable evolution - Edge-based and region-based energy minimization approaches - Numerical schemes for solving level set equations - Visualization methods for tracking segmentation progress This resource proves particularly valuable for medical researchers and practitioners seeking to implement level set-based MRI brain image segmentation with robust computational methods and practical guidance.